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Compliant net for AUV retrieval using a UAV

IFAC-PapersOnLine 2018年第29期51卷 431-437页

作者： Ivanovic, Antun Polic, Marsela Salah, Omar Orsag, Matko Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10 000 Croatia Mechanical Engineering Department Faculty of Engineering Assiut University Assiut Egypt

This paper addresses the problem of retrieving an autonomous underwater vehicle (AUV) with an unmanned aerial vehicle (UAV). The design of the compliant net that naturally adapts to the payload morphology is proposed as a retrieval tool. We constructed the proposed compliant net and conducted experiments with an underwater vehicle model as payload. Furthermore, mathematical model of a system comprised of the UAV and the payload is presented and a mid-ranging controller is utilized for such a system. We also present simulation results and analyze the stability of the UAV with the cable attached payload. © 2018

关键词： Autonomous underwater vehicles Antennas Unmanned aerial vehicles (UAV) Autonomous underwater vehicles (AUV) Marine systems Mechanical design Retrieval tool Soft robotics Underwater vehicles

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Self-organizing visual perception for mobile robot navigation 1

Self-organizing visual perception for mobile robot navigatio...

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1st Euromicro Workshop on Advanced Mobile Robots, EUROBOT 1996

作者： Von Wichert, Georg Control Systems Theory and Robotics Department Technical University Darmstadt Landgraf-Georg-Str.4 DarmstadtD-64283 Germany

ISBN: (纸本)0818676957

Visual navigation for mobile robots is almost always performed on the basis of complex CAD models which have to be given to the system in advance. There are some approaches that map visual information more or less directly to robot actions in order to implement some basic capabilities like road following or docking. Complex navigation task, e.g. in buildings, however, are usually solved using three dimensional models of the environment or the objects within it. This paper presents a method for self-organizing the visual perception of a mobile robot in order to adapt it to the surroundings without the need to define and model the relevant aspects of the environment. The system uses a self-organization process to transform the continuous pour of images into a limited number of discrete perceptions which can be used far navigation purposes. Results obtained in an indoor office environment are presented. Nevertheless, the approach is suited also for outdoor navigation. © 1996 IEEE.

关键词： Mobile robots

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Application of Statistical Methods to Support Automation of Pricing in Business 21st

Application of Statistical Methods to Support Automation o...

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21st Polish control Conference, PCC 2023

作者： Grobler-Debska, Katarzyna Kucharska, Edyta Mularczyk, Rafal Jagodziński, Mieczyslaw Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland Department of Systems Biology and Engineering Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031351723

This paper presents the possibility of using statistical modeling to automate the process of dynamic pricing management with revenue control and adaptation to current legislation in this area. In addition, it is proposed the use of integration of Python tools with the ERP and BI systems popularly used in the industry. This integration in order to facilitates decision-making in this area for the business without the end of programming. The proposed solutions support the CEO enterprises make decisions regarding pricing strategies and optimize their revenues. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Variable stiffness mechanisms using spherical continuously variable transmissions

Variable stiffness mechanisms using spherical continuously v...

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ASME 2015 International Mechanical Engineering Congress and Exposition, IMECE 2015

作者： Capehart, Twan Moore, Carl A. Center for Intelligent Systems Control and Robotics Department of Mechanical Engineering FAMU-FSU College of Engineering TallahasseeFL32310 United States

ISBN: (纸本)9780791857397

Some robot developers are considering elasticity to provide compliance for better adaption to a changing environment, shock resistance and safer human-robotic interactions (HRI). In this study we simulate a spherical continuously variable transmission (CVT) to validate its ability as the primary mechanism in a variable stiffness device for a 1D robotic hopper. The spherical CVT has been used in many robotic applications including cobots by Peshkin et al. [1] and as the driving unit in the load sensitive mechanism by Tadakuma et al. [2]. A CVT based variable stiffness/damping device intended for vibration mitigation was presented by Little [3]. That paper presented the kinematics and experiments of the CVT based variable stiffness/damping device for the specific application of vibration mitigation. This study considers the dynamics of the CVT based variable stiffness/damping device and modifies the device based on previous studies. We use ADAMS to simulate the modified system because it captures many of the real world dynamics arising from the CVT's rolling friction dynamics. Finally we present a conceptual design of the variable stiffness CVT and briefly discuss its use in a 1D legged hopper application. Copyright © 2015 by ASME.

关键词： Spheres

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on Computer Vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Navigating the Future: Advancing Autonomous Vehicles through Robust Target Recognition and Real-Time Avoidance 4

Navigating the Future: Advancing Autonomous Vehicles through...

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4th International Conference on control Theory and Applications, ICoCTA 2024

作者： Hussein, Mohammed A. M. Habib, Maki K. The American University in Cairo Robotics Control and Smart Systems Cairo Egypt The American University in Cairo Department of Mechanical Engineering Cairo Egypt

ISBN: (纸本)9798331506155

This research addresses the critical challenge of enhancing object recognition and real-Time response capabilities in autonomous vehicles (AVs) under varying simulated conditions, which is crucial for ensuring both navigational safety and operational efficiency. Utilizing the Carla 0.9.14 simulator and Unreal Engine 4.26 on Ubuntu 20.04, we focus on improving the detection and classification of key on-road obstacles-vehicles, pedestrians, and cyclists-using the YOLOv7 object detection algorithm. By integrating advanced sensory technologies, specifically stereo vision cameras and LIDAR, we create a dynamic testing environment that simulates diverse urban and rural scenarios. Our methodology enhances the YOLOv7 algorithm's accuracy and speed through extensive training on a meticulously curated dataset of 4,113 images, reflecting a broad spectrum of environmental conditions, including varying lighting and weather conditions. This rigorous approach yielded a significant increase in mean average precision (mAP) from 64.3% to 76.3%, and enhanced the algorithm's reliability, with notable improvements over previous models. The research delineates a clear advancement in AV technology by demonstrating substantial improvements in object detection metrics, contributing foundational insights for future implementations in real-world settings and supporting the further development of real-Time avoidance systems. This study not only progresses the field of AV but also sets a new benchmark for object detection performance, aligning with industry and academic goals to optimize AV systems for complex and unpredictable driving scenarios. © 2024 IEEE.

关键词： Stereo image processing

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A Vision Based Hardware-Software Real-Time control System for the Autonomous Landing of an UAV 1

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International Conference on Computer Vision and Graphics, ICCVG 2020

作者： Blachut, Krzysztof Szolc, Hubert Wasala, Mateusz Kryjak, Tomasz Gorgon, Marek Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

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Optimisation of a Siamese Neural Network for Real-Time Energy Efficient Object Tracking 1

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International Conference on Computer Vision and Graphics, ICCVG 2020

作者： Przewlocka, Dominika Wasala, Mateusz Szolc, Hubert Blachut, Krzysztof Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper the research on optimisation of visual object tracking using a Siamese neural network for embedded vision systems is presented. It was assumed that the solution shall operate in real-time, preferably for a high resolution video stream, with the lowest possible energy consumption. To meet these requirements, techniques such as the reduction of computational precision and pruning were considered. Brevitas, a tool dedicated for optimisation and quantisation of neural networks for FPGA implementation, was used. A number of training scenarios were tested with varying levels of optimisations – from integer uniform quantisation with 16 bits to ternary and binary networks. Next, the influence of these optimisations on the tracking performance was evaluated. It was possible to reduce the size of the convolutional filters up to 10 times in relation to the original network. The obtained results indicate that using quantisation can significantly reduce the memory and computational complexity of the proposed network while still enabling precise tracking, thus allow to use it in embedded vision systems. Moreover, quantisation of weights positively affects the network training by decreasing overfitting. © 2020, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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Synthesis of Diagnostic Observers for Linear systems Based on Optimal control Methods

Synthesis of Diagnostic Observers for Linear Systems Based o...

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2022 International Conference on control, Automation and Diagnosis, ICCAD 2022

作者： Kabanov, Aleksey A. Zuev, Aleksandr V. Zhirabok, Aleksey N. Filaretov, Vladimir F. Department of Informatics and Control in Technical Systems Sevastopol State University Russia Laboratory of Intelligent Information Systems for Marine Robots Institute of Marine Technology Problems FEB RAS Russia Department of Automation and Robotics Far Eastern Federal University Russia Robotics Laboratory Institute of Automation and Control Processes FEB RAS Russia

ISBN: (数字)9781665497947

ISBN: (纸本)9781665497947

The paper considers the problem of fault identification in dynamic systems described by linear differential equations. To solve this problem, diagnostic observers constructed on the basis of optimal control methods are used. The proposed new method for the synthesis of such observers is based on the idea of introducing a new system with some auxiliary control which plays a role of an unknown vector function describing emerging faults. This control is proposed to be formed in such a way that it provides a minimum for the residual functional. Unlike popular approaches to solving the problem of fault identification based on observers operating in a sliding mode, the proposed method allows us to expand the class of systems for which the identification problem can be solved. It is known that the methods of sliding mode observers design impose certain restrictions on the systems under consideration. The suggested theory is illustrated by an example. The simulation results confirmed the operability of the diagnostic observers synthesized using the method proposed in the work. © 2022 IEEE.

关键词： Linear systems

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